Detecting hotspots of ecosystem change with remote sensing across the Arctic
Bibliographic record
Abstract
The Arctic region is warming faster than elsewhere on Earth, at a rate nearly twice the global average. This warming is expected to negatively impact vegetation, hydrology, terrain thaw, and many other ecosystem properties. Here we identify primary hotspots of landscape changes occurring across the Arctic using multiple observations from reanalysis and satellite remote sensing, spanning visible, near-infrared, and thermal infrared (VIS-NIR-TIR) and microwave bands. This suite of VIS-NIR-TIR and microwave-derived products allows for the longer-term monitoring of ecological indicators for climate (e.g., temperature and precipitation), landscape surface frozen status, ecosystem water stress, and vegetation. Specifically, we examined “hotspots” (i.e., Getis-Ord Gi* statistics) and associated rates of change in thermal state, including near-surface air temperature; annual start and length of the surface non-frozen period; soil thaw depth. To identify regional changes in wetness, we examined hotspots of change and trends in precipitation; snow cover; surface water inundation; soil moisture status. For vegetation, we examined VIS-NIR greenness indices; annual start date and length of growing season; history of disturbance (i.e., fires). Lastly, we examined higher (30 m) resolution Landsat and Sentinel 2 imagery and in situ observations to better understand the drivers of change and the potential impacts to local communities and infrastructure. Our hotspot analysis indicated the most severe changes occurring in the Russian Far East, the Northwest Territories of Canada, and portions of Alaska including the North Slope. Specifically, the Northwest Territories have experienced warming, greening and wetting while the Russian Far East has experienced large temperature increases, an increase in permafrost active layer thickness, and a potential lengthening of the non-frozen season (as indicated by the classification of the ground surface state by microwave remote sensing). The North Slope of Alaska has experienced increasing temperatures, precipitation and a decrease in the number of frozen days per year. Information obtained through this remote sensing analysis, integrated into a geographic information system, can be used to better support decision making for land management and risk assessments across the rapidly warming Arctic-boreal region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".